[igraph] semi-supervized mincut

[Renato Fabbri]

hi list,

if
g=igraph.Graph()

we have
g.mincut(capacity=None) and g.mincut_value(source=-1, target=-1, capacity=None)
methods.

the thing is that mincut() returns the partitions, but you cannot
specify the nodes one wants in each partition.

On the other hand, mincut_value() returns only the value, not the partitions.

this makes it very odd (afaik impossible) to implement the mincut
semi-supervised learning method such as described here:
http://www.google.com/url?sa=t&source=web&cd=1&ved=0CBIQFjAA&url=http%3A%2F%2Fwww.cs.cmu.edu%2F~avrim%2FPapers%2Fmincut.ps&ei=OmkyTOd4wZyWB46z3MAL&usg=AFQjCNHWepNiPTGMk2FdUM-X5VVEtKk2Hg

For a two class problem, one needs to specify at least that one node
of each class should be in each partition, and have the partitions,
not the value of the cut itself.

It seems to me that the g.mincut_value() finds the partitions but
discards it instead of returning the nodes related to each partition.

any idea?

cheers,
rf

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